Background COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situation culminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. The pandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern, forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, having knowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view to accordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemic to both public health and economic sectors. Methods Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory (LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm Convolutional Neural Network - Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, South Africa and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developed for 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. Results The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAE and RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of 98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesser MSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Conclusions Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infection cases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but also in terms of lesser MSE, MAE and RMSE.

Grid scheduling is one of the prime challenges in grid computing. Reliability, efficiency (with regard to time utilization), effectiveness in resource usage, as well as robustness tend to be the demanded features of Grid scheduling systems. A number of algorithms have been designed and developed in order to make effective Grid scheduling. Project management is the well known area of operation research. Here we are proposing a new prioritized deadline based scheduling algorithm (PDSA) using project management technique for efficient job execution with deadline constraints of users' jobs. An extensive performance comparison has been presented using synthetic workload traces to evaluate the efficiency and robustness of grid scheduling with respect to average turnaround times and maximum tardiness. Result has shown improved performance under dynamic scheduling environment. Based on the comparative performance analysis, proposed PDSA has shown the optimal performance as compared to EDF and RR scheduling algorithms under dynamic scheduling environment. In brief, PDSA is the true application of project management in grid computing.

In recent years, increasing demand for computing has led to the development of computational grid. Typically scheduling challenges tend to be NP-hard problems where there is no optimal solution. The research reported here therefore is focused on the development of hybrids scheduling algorithms based on deadline and slack time parameters and its variations, using the concept of optimization techniques. An extensive performance comparison has been presented using real workload traces as benchmark on a grid computational environment. The results were compared with some baseline scheduling approaches in extant literature. The results have shown that the performances of grid scheduling algorithms developed and reported in this paper give good results in most of the cases and also support true scalability, when in the scenario of increasing workload and number of processors on a computational grid environment.

Over the decades people have seen a change from large mainframe computing to commodity, off-the-shelf clusters of high performance computers. Currently data centers have thousands or tens of thousands of high performance computers that provides services as well as computation for tens or hundreds of thousands of users. Traditional IT challenges such as scheduling, resource allocation and now data centers are dealing with power consumption, it is the most expensive operational cost factor in data centers. Inefficient cooling leads to high temperature and this in turn leads to hardware failure. In this paper a Hybrid Integrated Thermal Aware Scheduling Algorithms are proposed based on baseline approaches. The aim of this paper is to minimize cooling energy consumption in data center labs when assigning jobs for computation. These algorithms avoid high thermal stress situations such as large hotspots and thermal violations events. The results show that the Hybrid Integrated Thermal aware scheduling algorithms (TFCFS and TRR) reduces cooling electricity by 7000KW compared to the baseline job scheduling algorithms, First Come First Serve (FCFS) and Round Robin (RR).